Explore MATLAB source code curated for "降噪" with clean implementations, documentation, and examples.
A beginner-friendly audio processing program utilizing Fast Fourier Transform (FFT) for spectral analysis and implementing noise reduction algorithms, serving as an ideal experimental framework for learning digital signal processing fundamentals.
Implementing singular value decomposition on a signal to identify optimal reconstruction points through differential spectrum analysis, achieving effective noise reduction while preserving key signal characteristics through selective component reconstruction.
Denoising with Principal Components After Singular Value Decomposition with Algorithm Implementation Details
This program implements sliding window-based smoothing and noise reduction for 1D curves, allowing users to select appropriate window widths based on useful signal frequencies while providing visualization of processing results.
This simulation algorithm implements image denoising based on the paper "Image Denoising using Scale Mixtures of Gaussians in the Wavelet Domain", featuring wavelet domain processing with Gaussian scale mixtures and optimized implementation for enhanced image quality.
Implementing SVD-based noise reduction for Lorenz signals through matrix decomposition to extract principal signal features and eliminate noise components.
Wavelet modulus maxima source code for wavelet denoising and modal parameter identification, originally developed by Stanford University with robust algorithmic implementations.
Filtering data from a MAT file followed by denoising using Singular Value Decomposition. The SVD denoising methodology references literature provided in the attachment, with enhanced descriptions of code implementation approaches and key algorithmic steps.
Implementing Inverse Fourier Transform Prior to Constructing Filters with Different Bandwidths
Implementing noise reduction techniques and distinguishing between human voices and machine-generated sounds using MATLAB's audio processing capabilities.